Soil Drainage and Productivity Indexes
This webpage provides information on the Drainage Index (DI) and Productivity Index (PI) of all soils that are classified within the US system of Soil Taxonomy. These data aid in the identification of areas at risk to various forest insects and diseases because of their ability to identify regions of potential tree stress (see the 2013-2027 National Insect and Disease Forest Risk Assessment).
For help with soil taxonomy terminology, please visit NRCS Soil Taxonomy. This work was performed under the supervision of Dr. Randall Schaetzl, Department of Geography, Environment, and Spatial Sciences of Michigan State University, under contract with (and supported by) the US Forest Service.
Please note that the tabular and GIS data downloads from this site have been updated using the 2023 gNATSGO database.
Soil Drainage Index
The Drainage Index (DI), originally named the "natural soil wetness index" (Hole and Campbell 1986, Schaetzl 1986), is a measure of long-term soil wetness. It is designed to represent, as an ordinal number, the amount of water that a soil contains and makes available to plants under normal climatic conditions. It is not meant to mimic the concept of "plant available water", which is mostly dependent upon soil texture. The DI only loosely/secondarily takes soil texture into consideration. The main factor affecting the DI is the depth to the water table and the soil volume available for plants to root and grow in. The DI concept was first initiated by Hole (1978) and Hole and Campbell (1985), and expanded upon by Schaetzl (1986).
The DI ranges from 0 to 99. The higher the DI, the more water the soil can and does, theoretically, supply to plants. Sites with DI values of 99 are essentially open water. A DI of zero indicates impermeable surfaces like bare bedrock or urban areas dominated by pavement and buildings. The DI is derived from the soil's taxonomic subgroup classification in the US system of Soil Taxonomy, and (optionally) its soil map slope class.
Because a soil's taxonomic classification is not (initially) affected by such factors as irrigation or artificial drainage, the DI does not change as soils become irrigated or drained (unless the long-term effects of this involve a change in the soil's taxonomic classification). Instead, the DI reflects the soil's NATURAL wetness condition. Each soil SERIES has, in theory, its own unique DI. Some soil series span two or more drainage classes; in this case the DI that is used is the one that would normally be used for a soil with that subgroup classification.
Schaetzl, R.J., Krist, F.J. Jr., Stanley, K.E., and C.M. Hupy. 2009. The Natural Soil Drainage Index: An Ordinal Estimate of Long-Term, Soil Wetness. Physical Geography 30:383-409. (3.5 MB PDF)
Soil Productivity Index
Like the DI, the Productivity Index (PI) is an ordinal measure, but of the productivity of a soil. The PI uses family-level Soil Taxonomy information, i.e., interpretations of taxonomic features or properties that tend to be associated with low or high soil productivity, to rank soils from 1 (least productive) to 19 (most productive) a PI of 0 are non-soil locations (water, impermeable surfaces.
The index has wide application, because, unlike competing indexes, it does not require copious amounts of soil data, e.g., pH, organic matter, or CEC, in its derivation. GIS applications of the PI, in particular, have great potential. For regionally extensive applications, the PI may be as useful and robust as other productivity indexes that have much more exacting data requirements.
Schaetzl, R.J. Krist, F.J. Jr., and B.A. Miller. 2012. A Taxonomically Based, Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses. Soil Science 177:288-299. (PDF, 7.1 MB)
References
Hole, F.D. 1978. An approach to landscape analysis with emphasis on soils. Geoderma 21:1-13.
Hole, F.D. and J.B. Campbell. 1985. Soil Landscape Analysis. Rowman and Allanheld, Totowa, NJ 196 pp.
Schaetzl, R.J. 1986. A soilscape analysis of contrasting glacial terrains in Wisconsin. Annals Assoc. Am. Geogs. 76:414-425.
Contacts
For more details about the DI and/or PI workings and theory, to request more detailed copies of posters or have questions answered, contact Dr. Randall Schaetzl at the Department of Geography, Environment, and Spatial Sciences of Michigan State University.
Data Downloads
Soil Drainage Index (DI) and Productivity Index (PI) Map Unit and Component lookup tables were updated in August of 2024 using a 2023 gNATSGO database download from the USDA NRCS Soils download site (https://nrcs.app.box.com/v/soils/). The updated Map Unit DI and PI lookup table were then joined to the 30-meter MURASTER raster file included in the 2023 CONUS gNATSGO file geodatabase.
The DI and PI rasters for the lower 48 states and Washington DC (CONUS) are available for download as zipped geotiffs. If users experience difficulties downloading these large, CONUS-wide, files we also provide the DI and PI 30-meter rasters in smaller, multi-state partitions.
Users can obtain Map Unit polygon vectors for individual states from the USDA NRCS soils download site. The state-level MUPOLYGON map unit polygon layers can be joined and related to the Map Unit and Component DI_PI tables in the ‘L48_MapUnits_Components_w_DI_PI_2023.gdb’ file geodatabase based on the MUKEY join field.
NOTE: The vast majority of these circa 2023 DI and PI lookups will properly join to more current versions of USDA soil MURASTER and MUPOLY layers. However newly soil surveyed areas will receive new MUKEYs that will have no join to these 2023 tables. And in some cases, soil properties of existing MUKEYs will get updates that could cause 2023 DI and PI values for those MUKEY joins to be outdated.
In addition to the zipped file geodatabase download, DI and PI Map Unit and Component lookup tables are also available in an Excel format. Also downloadable, is an Excel Soil Series table with fields added for series DI and PI values. Based on matches of soil taxonomy the 2023 Soil Series and Map Unit/Component DI and PI Excel workbooks could be used to determine DI and PI values for new (post 2023) soil survey Map Units.
| DESCRIPTION | FILE TYPE/SIZE |
|---|---|
| DI and PI 30-meter raster README | DOCX, 20 KB |
| DI-PI Map Unit & Component join tables (GDB file) | ZIP, 21 MB |
| DI-PI Map Unit & Component join tables (Excel file) | XLSX, 72 MB |
| DI modifiers table | PDF, 87 KB |
| PI modifiers table | PDF, 122 KB |
| DI and PI by Soil Series | XLSX, 4 MB |
| REGION | STATES | DI ZIP FILE/SIZE | PI ZIP FILE/SIZE |
|---|---|---|---|
| CONUS | All states and Washington DC in Continental US | DI, 1.5 GB | PI, 1.4 GB |
| Northcentral | Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin | DI, 383 MB | PI, 334 MB |
| Northcentral West | Montana, Nebraska, North Dakota, South Dakota, Wyoming | DI, 251 MB | PI, 236 MB |
| Northeast | Connecticut, Delaware, District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia | DI, 171 MB | PI, 147 MB |
| Northwest | Idaho, Oregon, Washington | DI, 83 MB | PI, 79 MB |
| Southcentral West | Colorado, Kansas, New Mexico, Oklahoma, Texas | DI, 278 MB | PI, 267 MB |
| Southeast | Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee | DI, 245 MB | PI, 223 MB |
| Southwest | Arizona, California, Nevada, Utah | DI, 126 MB | PI, 122 MB |
Research Links
Pi-Di Citations
A selection of papers and theses that have cited/used the Productivity and Drainage indices.
Soil Productivity Index and Soil Drainage Index
Costanza, J.K., Faber-Langendoen, D., Coulston, J.W. and D.N. Wear. 2018. Classifying Forest Inventory Data into Species-based Forest Community Types at Broad Extents: Exploring Tradeoffs among Supervised and Unsupervised Approaches. Forest Ecosystems 5:1-17.
Connallon, C.B. and R.J. Schaetzl. 2017. Geomorphology of the Chippewa River Delta of Glacial Lake Saginaw, Central Lower Michigan, USA. Geomorphology 290:128-141.
Gadoth-Goodman, D. 2017. Can Short-Rotation Harvests Increase Management Options for the Endangered Kirtland's Warbler? Master’s Thesis, Michigan State University, East Lansing.
Ingwell, L.L., Lacroix, C., Rhoades, P.R., Karasev, A.V. and N.A. Bosque-Pérez. 2017. Agroecological and Environmental Factors Influence Barley Yellow Dwarf Viruses in Grasslands in the US Pacific Northwest. Virus Res. 241:185-195.
Wilson, D.C. and A.R. Ek. 2017. Imputing Plant Community Classifications for Forest Inventory Plots. Ecol. Indicators, 80:327-336.
Wilson, D.C. and A.R. Ek. 2017. Imputing Plant Community Classification from Associated Forest Inventory and Physiographic Data in Minnesota, USA. Ecol. indicators, 79:73-82.
Wilson, D. 2016. Imputation of Ecological Detail using Associated Forest Inventory, Plant Community and Physiographic Data. PhD Dissertation, University of Minnesota, Minneapolis.
Deo, R.K. 2014. Application of an Imputation Method for Geospatial Inventory of Forest Structural Attributes across Multiple Spatial Scales in the Lake States, USA. PhD Dissertation, Michigan Technological University, Houghton.
Phillips, J.D. 2013. Evaluating Taxonomic Adjacency as a Source of Soil Map Uncertainty. Eur. J. Soil Sci. 64:391-400.
Pitel, N.E. 2010. An Assessment of Sugar Maple Condition following Defoliation by Forest Tent Caterpillar: Investigating Soil Chemistry. Master’s Thesis, New York State University, New York.
Soil Productivity Index
Bush, E., 2019. Development of a Dryland Corn Productivity Index for Kansas. Master’s Thesis, Kansas State University, Manhattan.
Darijani, F., Veisi, H., Liaghati, H., Nazari, M.R. and K. Khoshbakht. 2019. Assessment of Resilience of Pistachio Agroecosystems in Rafsanjan Plain in Iran. Sustainability 11:1-14.
Kim, T.J., Wear, D.N., Coulston, J., and R.H. Li. 2019. Forest Land Use Responses to Wood Product Markets. For. Policy and Econ. 93:45-52.
Iverson, L.R., Peters, M.P., Prasad, A.M. and S.N. Matthews. 2019. Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling. Forests 10:302.
Ge, M., Edwards, E.C. and S. Akhundjanov. 2018. Land Ownership and Irrigation on American Indian Reservations. CEnREP Working Paper No.18-017:1-56.
Kim, T.J., Wear, D.N., Coulston, J. and R. Li. 2018. Forest Land Use Responses to Wood Product Markets. Forest Policy and Econ. 93:45-52.
Ayram, C.A.C., Mendoza, M.E., Etter, A. and D.R.P. Salicrup. 2017. Anthropogenic Impact on Habitat Connectivity: A Multidimensional Human Footprint Index Evaluated in a Highly Biodiverse Landscape of Mexico. Ecol. Indicators 72:895-909.
Bouza P.J., Saín C., Videla L., Dell’Arciprete P., Cortés E., and J. Rua. 2017 Soil–Geomorphology Relationships in the Pichiñán Uraniferous District, Central Region of Chubut Province, Argentina. In: Rabassa J. (ed.), Advances in Geomorphology and Quaternary Studies in Argentina. Springer Earth System Sciences. Springer. pp. 77-99.
Hengl, T., Leenaars, J.G., Shepherd, K.D., Walsh, M.G., Heuvelink, G.B., Mamo, T., Tilahun, H., Berkhout, E., Cooper, M., Fegraus, E. and I. Wheeler. 2017. Soil Nutrient Maps of Sub-Saharan Africa: Assessment of Soil Nutrient Content at 250 m Spatial Resolution Using Machine Learning. Nutrient Cycling in Agroecosystems 109:77-102.
Ibáñez, I., Katz, D.S. and B.R. Lee. 2017. The Contrasting Effects of Short-term Climate Change on the Early Recruitment of Tree Species. Oecologia 184:701-713.
Marko, O., Brdar, S., Panić, M., Šašić, I., Despotović, D., Knežević, M. and V. Crnojević. 2017. Portfolio Optimization for Seed Selection in Diverse Weather Scenarios. PloS ONE 12:1-27.
Shrestha, P. 2017. Importance of Concentrated Flow Paths in Agricultural Watersheds of Southern Illinois. Master’s Thesis, Southern Illinois University, Carbondale.
Schaetzl, R.J. 2017. Soils of the Northern Lake States Forest and Forage Region. In: The Soils of the USA. West, L.T., M.J. Singer, and A. Hartemink (eds.), Springer, New York. pp. 191-208.
National Academies of Sciences, Engineering, and Medicine, 2016. Pathways to Urban Sustainability: Challenges and Opportunities for the United States. National Academies Press.
Prasad, A.M., Iverson, L.R., Matthews, S.N. and M.P. Peters. 2016. A Multistage Decision Support Framework to Guide Tree Species Management under Climate Change via Habitat Suitability and Colonization Models, and a Knowledge-based Scoring System. Landscape Ecol. 31:2187-2204.
Albano, C.M. 2015. Identification of Geophysically Diverse Locations that may Facilitate Species’ Persistence and Adaptation to Climate Change in the Southwestern United States. Landscape Ecol. 30:1023-1037.
Bonfante, A. and J. Bouma. 2015. The Role of Soil Series in Quantitative Land Evaluation when Expressing Effects of Climate Change and Crop Breeding on Future Land Use. Geoderma 259:187-195.
Krohn, B., 2015. Switching to Switchgrass: Pathways and Consequences of Bioenergy Switchgrass entering the Midwestern Landscape. PhD Dissertation, University of Minnesota, Minneapolis.
Prasad, A.M. 2015. Macroscale Intraspecific Variation and Environmental Heterogeneity: Analysis of Cold and Warm Zone Abundance, Mortality, and Regeneration Distributions of Four Eastern US Tree Species. Ecol. and Evolution 5:5033-5048.
Coleman, T.W., Jones, M.I., Courtial, B., Graves, A.D., Woods, M., Roques, A. and S.J. Seybold. 2014. Impact of the First Recorded Outbreak of the Douglas Fir Tussock Moth, Orgyia pseudotsugata, in Southern California and the Extent of its Distribution in the Pacific Southwest Region. For. Ecol. Mgmt. 329:295-305.
Häring, T., Reger, B., Ewald, J., Hothorn, T. and B. Schröder. 2014. Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps–from Averages to Multivariate Spectra. Folia Geobot. 49:385-405.
Jamroz, E., Weber, J. and M. Dębicka. 2014. Trophic Soil Index of the Rusty Soils Affected by Clear-Cutting in the Spała Forest District. Sylwan 158:669-674.
Kim, K.S., Kim, S.J., Do Park, K., Lee, C.W., Ryu, J.H., Choi, J.S., Jeon, W.T., Kang, H.W. and M.T. Kim. 2014. Assessment of Sustainable Production on Paddy Field Treated with Green Manure Crops Using Sustainability Index. 한국토양비료학회지 47:165-171.
Li, H., 2014. Farmers' Switchgrass Adoption Decision under Different Market Scenarios - An Agent Based Modeling Approach. Master’s Thesis, Michigan State University, East Lansing.
Ligmann-Zielinska, A., Kramer, D.B., Cheruvelil, K.S. and P.A. Soranno. 2014. Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve their Analytical Performance and Policy Relevance. PLOS ONE 9(10): e109779. https://doi.org/10.1371/journal.pone.0109779
Häring, T. 2013. Spatial Prediction Methods for the Assessment and Mapping of Forest Site Characteristics. PhD Dissertation, Technische Universität, München.
Soil Drainage Index
Hofmeister, K.L., Nave, L.E., Riha, S.J., Schneider, R.L. and M.T. Walter. 2019. A Test of Two Spatial Frameworks for Representing Spatial Patterns of Wetness in a Glacial Drift Watershed. Vadose Zone J. 18:1-17.
Blewett, W.L., Lusch, D.P., Schaetzl, R.J., and S.A. Drzyzga. 2018, A Century of Change in the Methods, Data, and Approaches to Mapping Glacial Deposits in Michigan. In: Kehew, A.E., and B.B Curry (eds.), Quaternary Glaciation of the Great Lakes Region: Process, Landforms, Sediments, and Chronology. Geol. Soc. Am. Spec. Paper 530:39–67.
Calabrese, S., Richter, D.D. and A. Porporato. 2018. The Formation of Clay-Enriched Horizons by Lessivage. Geophys. Res. Letters 45:7588-7595.
Malone, B.P., McBratney, A.B. and B. Minasny. 2018. Description and Spatial Inference of Soil Drainage using Matrix Soil Colours in the Lower Hunter Valley, New South Wales, Australia. PeerJ:1-20.
Salfer, J.T. 2018. Modeling Pre-Settlement Wetlands in Northern Minnesota. Master’s Thesis, Minnesota State University, Mankato.
Luehmann, M.D. and R.J. Schaetzl. 2017. Late Pleistocene Deltas in the Lower Peninsula of Michigan, USA. In: Kehew, A.E., and B.B. Curry (eds.), Quaternary Glaciation of the Great Lakes Region: Process, Landforms, Sediments, and Chronology. Geol. Soc. Am. Spec. Paper 530:163-177.
Hill, B.H., Kolka, R.K., McCormick, F.H. and M.A. Starry. 2014. A Synoptic Survey of Ecosystem Services from Headwater Catchments in the United States. Ecosystem Services 7:106-115.
Kowal, V.A., Schmolke, A., Kanagaraj, R. and D. Bruggeman. 2014. Resource Selection Probability Functions for Gopher Tortoise: Providing a Management Tool Applicable across the Species’ Range. Environ. Mgmt. 53:594-605.
Scherr, S.J., Buck, L., Willemen, L., and J.C. Milder. 2014. Ecoagriculture: Integrated Landscape Management for People, Food, and Nature. In: Van Alfen N., Encyclopedia of Agriculture and Food Systems 3:1-17.
Chase, K.D. 2013. Forest Stand Preference of Sirex nigricornis, and Sirex noctilio Hazard in the Southeastern United States. PhD Dissertation, Mississippi State University, Starkville.
Schaetzl, R.J., Enander, H., Luehmann, M.D., Lusch, D.P., Fish, C., Bigsby, M., Steigmeyer, M., Guasco, J., Forgacs, C. and A. Pollyea. 2013. Mapping the Physiography of Michigan with GIS. Phys. Geog. 34:2-39.
Shartell, L.M., Lilleskov, E.A. and A.J. Storer. 2013. Predicting Exotic Earthworm Distribution in the Northern Great Lakes region. Biol. Invasions 15:1665-1675.
Touchstone, R.B. 2013. Automated Template C: Created by James Nail 2011V2. 01. PhD Dissertation, Mississippi State University, Starkville.
Mikesell, L.R. 2012. Lithostratigraphic Correlation at Various Spatial Scales in the Livermore Basin at the Lawrence Livermore National Laboratory, California, USA. Michigan State University. PhD Dissertation, Michigan State University, East Lansing.
Olson, M.G. 2012. Remote Sensing of Forest Health Trends in the Northern Green Mountains of Vermont. Master’s Thesis, University of Vermont, Burlington.
Pitel, N.E. 2010. An Assessment of Sugar Maple Condition following Defoliation by Forest Tent Caterpillar: Investigating Soil Chemistry. Master’s Thesis, New York State University, New York.